Primer acercamiento a la mecánica de contacto en amputados transfemorales unilaterales

Publicado

2017-07-01

Modelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisión

Optimization models for power systems in the evolution to smart grids: A review

DOI:

https://doi.org/10.15446/dyna.v84n202.63354

Palabras clave:

optimización, energías renovables, redes inteligentes, flujo óptimo de potencia (es)
optimization, renewable energies, smart grids, optimal power flow (en)

Descargas

Autores/as

El presente artículo describe los modelos de optimización recientemente aplicados al diseño y operación de los sistemas de potencia hacia la conformación de las redes inteligentes e identifica las tendencias, barreras y posibles brechas en esta área. Se describen modelos para optimizar el diseño y la operación de los sistemas de potencia considerando las energías renovables, la generación distribuida, las micro redes, la gestión de la demanda y los sistemas de almacenamiento de energía. Se concluyó que es necesario validar muchos de los modelos que se han formulado recientemente para la optimización de la operación mediante pruebas con datos reales y a gran escala. Además, la gestión de la demanda y las micro redes son aspectos en los cuales se requieren desarrollar modelos para el flujo óptimo de potencia. Finalmente, es necesario predecir con mayor precisión las variables estocásticas para que estos modelos se adapten al comportamiento real del sistema.
The present paper aims to describe the optimization models recently applied to the design and operation of power systems in the road to the formation of smart grids and to identify the trends, challenges and possible gaps existing in this field of study. The models described allow performing optimization of the design and operation of power systems considering aspects as renewable energies and its related variability, distributed generation and micro grids, demand-site management and energy storage systems. Conclusions point out that several of the models recently formulated need to be validated with real data and large-scale systems tests. Moreover, demand-site management and micro grids are aspects that lack of the development of complete optimal power flow models. Finally, the accurate forecasting of stochastic variables is necessary to accomplish a better adaptation of models to real behavior of the power system.

Referencias

Borlase, S., Smart grids: Infrastructure, technology, and solutions. CRC Press, 2016.

Electric Power Research Institute (EPRI), Report to NIST on the smart grid interoperability standards roadmap, EPRI, Contract No. SB1341-09-CN-0031-Deliverable, pp. 1-167, 2009.

National Institute of Standards and Technology, NIST Special Publication 1108 NIST Framework and roadmap for smart grid interoperability standards, 2010.

Pappu, V., Carvalho, M. and Pardalos, P.M., Optimization and security challenges in smart power grids. Springer, 2013.

Momoh, J.A., Electric power system applications of optimization, 53, 2001.

Calafiore, G.C. and Ghaoui, LE., Optimization models. Cambridge University Press, 2014.

Sánchez, P. et al., Modelos matemáticos de optimización, Madrid, 2010.

Renewable Energy Policy Network for the 21st Century (REN21), Renewables 2016: Global status report. 2016.

Arrillaga, J., Watson, N. and Liu, Y., Flexible power transmission the HVDC Options. John Wiley & Sons, Ltd, 2007.

European Technology Platform on Smartgrids, Consolidated view of the european platform on smartgrids, 2015.

U.S. Department of Energy, 2014 Smart grid system report(August. 2014.

Holttinen, H., Meibom, P. and Orths, A., Impacts of large amounts of wind power on design and operation of power systems, results of IEA collaboration, Wind Energy, 14(2), pp. 179-192, 2011. DOI: 10.1109/TPWRS.2010.2070848

Eftekharnejad, S., Vittal, V., Heydt, G.T., Keel, B. and Loehr, J., Impact of increased penetration of photovoltaic generation on power systems, Power Syst. IEEE Trans., 28(2), pp. 893-901, 2013. DOI: 10.1109/TPWRS.2012.2216294

Mahmoud, M.S. and AL-Sunni, F.M., Control and optimization of distributed generation systems, 2015.

Ackermann, T., Andersson, G. and Söder, L., Distributed generation: A definition, Electr. Power Syst. Res., 57(3), pp. 195-204, 2001. DOI: 10.1016/S0378-7796(01)00101-8

Li, R. and Zhou, F., Microgrid technology and engineering application. Elsevier Science & Technology Books, 2015.

Vanitha, R., Baskaran, J. and Sudhakaran, M., Multi Objective Optimal Power Flow with STATCOM using DE in WAFGP, Indian J. Sci. Technol., 8(2, pp. 191-198, 2015. DOI: 10.17485/ijst/2015/v8i1/56654

Kim, C.K., Sood, V.K., Jang, G.S., Lim, S.J. and Lee, S.J., HVDC Transmission: Power Conversion Applications in Power Systems. Wiley, 2009.

Carrizosa, M.J.J., Navas, F.D., Damm, G. and Lamnabhi-Lagarrigue, F.F., Optimal power flow in multi-terminal HVDC grids with offshore wind farms and storage devices, Int. J. Electr. Power Energy Syst., 65, pp. 291-298, 2015. DOI: 10.1016/j.ijepes.2014.10.016

Kondoh, J. et al., Electrical energy storage systems for energy networks, Energy Convers. Manag., 41(17), pp. 1863-1874, 2000.

Luo, X., Wang, J., Dooner, M. and Clarke, J., Overview of current development in electrical energy storage technologies and the application potential in power system operation, Appl. Energy, 137, pp. 511-536, 2015. DOI: 10.1016/j.apenergy.2014.09.081

Zhao, H., Wu, Q., Hu, S., Xu, H., Rasmussen, C.N. and Nygaard, C., Review of energy storage system for wind power integration support, Appl. Energy, 137, pp. 545-553, DOI: 2014. 10.1016/j.apenergy.2014.04.103

Losi, A., Mancarella, P. and Vicino, A., Integration of demand response into the electricity chain: Challenges, opportunities and smart grid solutions. Wiley, 2015.

U.S. Department of Energy, Benefits of demand response in electricity markets and recommendations for achieving them, 2009.

Cappers, P., Goldman, C. and Kathan, D., Demand response in U.S. electricity markets: Empirical Evidence, Berkeley, U.S.A., 2009.

Siano, P., Demand response and smart grids - A survey, renew. Sustain. Energy Rev., 30, pp. 461-478, 2014.

Fathima, A.H. and Palanisamy, K., Optimization in microgrids with hybrid energy systems - A review, renew. Sustain. Energy Rev., 45, pp. 431-446, 2015. DOI: 10.1016/j.rser.2015.01.059

Sinha, S. and Chandel, S.S., Review of software tools for hybrid renewable energy systems, renew. Sustain. Energy Rev., 32, pp. 192-205, 2014. DOI: 10.1016/j.rser.2014.01.035

Bhandari, B., Lee, K.-T., Lee, G.-Y., Cho, Y.-M. and Ahn, S.-H., Optimization of hybrid renewable energy systems: A review, Int. J. Precis. Eng. Manuf. Technol., 2(1), pp. 99-112, 2015.

Sinha, S. and Chandel, S.S., Review of recent trends in optimization techniques for solar photovoltaic-wind based hybrid energy systems, Renew. Sustain. Energy Rev., 50, pp. 755-769, 2015. DOI: 10.1016/j.rser.2015.05.040

Camargo, L.A.S., Ramos, D.S., Guarnier, E., Ishida, S. and Matsudo, E., Alternative generation sources portfolio: Optimal resource allocation and risk analysis supported by genetics algorithms, IEEE Lat. Am. Trans., 14(7), pp. 3232-3241, 2016.

Abdelaziz, A.Y., Hegazy, Y.G., El-Khattam, W. and Othman, M.M., Optimal allocation of stochastically dependent renewable energy based distributed generators in unbalanced distribution networks, Electr. Power Syst. Res., 119, pp. 34-44, 2015. DOI: 10.1016/j.epsr.2014.09.005

Wang, J. and Yang, F., Optimal capacity allocation of standalone wind/solar/battery hybrid power system based on improved particle swarm optimisation algorithm, IET Renew. Power Gener., 7(5), pp. 443-448, 2013.

Zhou, W., Lou, C., Li, Z., Lu, L. and Yang, H., Current status of research on optimum sizing of stand-alone hybrid solar-wind power generation systems, Appl. Energy, 87(2), pp. 380-389, 2010.

Rodrigues, S., Bauer, P. and Bosman, P.A.N., Multi-objective optimization of wind farm layouts - Complexity, constraint handling and scalability, Renew. Sustain. Energy Rev., 65, pp. 587-609, 2016.

Yang, J., Feng, X., Tang, Y., Yan, J., He, H. and Luo, C., A power system optimal dispatch strategy considering the flow of carbon emissions and large consumers, Energies, 8(9), pp. 9087-9106, 2015. DOI: 10.3390/en8099087

Summers, T., Warrington, J., Morari, M. and Lygeros, J., Stochastic optimal power flow based on conditional value at risk and distributional robustness, Int. J. Electr. Power Energy Syst., 72, pp. 116-125, 2015. DOI: 10.1016/j.ijepes.2015.02.024

Dufo-López, R. and Bernal-Agustín, J.L., Multi-objective design of PV-wind-diesel-hydrogen-battery systems, Renew. Energy, 33(12), pp. 2559-2572, 2008.

Ould-Bilal, B., Sambou, V., Ndiaye, P.A., Kébé, C.M.F. and Ndongo, M., Optimal design of a hybrid solar-wind-battery system using the minimization of the annualized cost system and the minimization of the loss of power supply probability (LPSP), Renew. Energy, 35(10), pp. 2388-2390, DOI: 2010. 10.1016/j.renene.2010.03.004

Martinez, G., Anderson, L.C., Anderson, L. and Martínez, G., Toward a scalable chance-constrained formulation for unit commitment to manage high penetration of variable generation, en 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014, pp. 723-730, 2014.

Taghavi, R., Reza, A. and Samet, H., Stochastic reactive power dispatch in hybrid power system with intermittent wind power generation, 89, 2015.

Atwa, Y.M. and El-Saadany, E.F., Probabilistic approach for optimal allocate on of wind-based distributed generation in distribution systems, IET Renew. Power Gener., 5(1), pp. 79-88, 2011. DOI: 10.1049/iet-rpg.2009.0011

Peng, X., Lin, L., Zheng, W. and Liu, Y., Crisscross optimization algorithm and monte carlo simulation for solving optimal distributed generation allocation problem, Energies, 8(12), pp. 13641-13659, 2015.

Shahzad, M., Ahmad, I., Gawlik, W. and Palensky, P., Load concentration factor based analytical method for optimal placement of multiple distribution generators for loss minimization and voltage profile improvement, Energies, 9(4), 2016.

Theo, W.L., Lim, J.S., Ho, W.S., Hashim, H. and Lee, C.T., Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods, Renew. Sustain. Energy Rev., 67, pp. 531-573, 2017. DOI: 10.1016/j.rser.2016.09.063

Ansarian, M., Sadeghzadeh, S.M. and Fotuhi-firuzabad, M., Optimum generation dispatching of distributed resources in smart grids, 2014.

Lasseter R., et al., The CERTS MicroGrid Concept, 2002.

Bhuiyan, F.A., Yazdani, A. and Primak, S.L., Optimal sizing approach for islanded microgrids, IET Renew. Power Gener., 9(2), pp. 166-175, 2015.

Ma, Y., Ji, J. and Tang, X., Triple-objective optimal sizing based on dynamic strategy for an islanded hybrid energy microgrid, Int. J. Green Energy, November, 2016.

Fossati, J.P., Galarza, A., Martín-Villate, A. and Fontán, L., A method for optimal sizing energy storage systems for microgrids, Renew. Energy, 77, pp. 539-549, 2015.

Ghiani, E., Vertuccio, C. and Pilo, F., Optimal sizing and management of a smart Microgrid for prevailing self-consumption, 2015 IEEE Eindhoven Power Tech. pp. 1-6, 2015. DOI: 10.1109/PTC.2015.7232554

Gamarra, C. and Guerrero, J.M., Computational optimization techniques applied to microgrids planning: A review, renew. Sustain. Energy Rev., 48, pp. 413-424, 2015.

Sanseverino, E.R., Di Silvestre, M.L., Badalamenti, R., Nguyen, N.Q., Guerrero, J.M. and Meng, L., Optimal power flow in islanded microgrids using a simple distributed algorithm, Energies, 8(10), pp. 11493-11514, 2015.

Zhao, B., Zhang, X., Chen, J., Wang, C. and Guo, L., Operation optimization of standalone microgrids considering lifetime characteristics of battery energy storage system, IEEE Trans. Sustain. Energy, 4(4), pp. 934-943, 2013. DOI: 10.1109/TSTE.2013.2248400

Rabiee, A. and Soroudi, A., Multi-period OPF model of power systems with HVDC connected intermittent wind power generation, IEEE Trans. Power Deliv., 29(1), pp. 336-344, 2014. DOI: 10.1109/TPWRD.2013.2259600

Kekatos, V., Wang, G., Conejo, A.J. and Giannakis, G.B., Stochastic reactive power management in microgrids with renewables, IEEE Trans. Power Syst., 30(6), pp. 3386-3395, 2015. DOI: 10.1109/TPWRS.2014.2369452

Ouammi, A., Dagdougui, H. and Sacile, R., Optimal control of power flows and energy local storages in a network of microgrids modeled as a system of systems, pp. 1-11, 2014.

Panda, A. and Tripathy, M., Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm, Energy, 93, pp. 816-827, 2015.

Taghavi, R., Seifi, A.R. and Pourahmadi-nakhli, M., Fuzzy reactive power optimization in hybrid power systems, Int. J. Electr. Power Energy Syst., 42(1), pp. 375-383, 2012. DOI: 10.1016/j.ijepes.2012.04.002.

Aragüés, M., Egea, A., Galceran-Arellano, S. and Gomis-Bellmunt, O., Optimal power flow tool for mixed high voltage alternating current and high-voltage direct current systems for grid integration of large wind power plants, IET Renew. Power Gener., 9(8), p. 876, 2015.

Aragüés-Peñalba, M., Beerten, J., Rimez, J., Van Hertem, D. and Gomis-bellmunt, O., Optimal power flow tool for hybrid AC/DC systems, in 11th IET Int. Conf. AC DC Power Transm., 2015.

Khraijah, M.M., Member, S., Arabia, S., Member, L. and Arabia, S., Optimization planning for large - scale offshore wind farms using multi - terminal HVDC transmission system, smart grid, pp. 1-4, 2015.

Rezaee-Jordehi, A., Brainstorm optimisation algorithm (BSOA): An efficient algorithm for finding optimal location and setting of FACTS devices in electric power systems, Int. J. Electr. Power Energy Syst., 69, pp. 48-57, 2015. DOI: 10.1016/j.ijepes.2014.12.083.

Lashkar-Ara, A., Kazemi, A. and Nabavi-Niaki, S.A., Multiobjective optimal location of FACTS shunt-series controllers for power system operation planning, IEEE Trans. Power Deliv., 27(2), pp. 481-490, 2012.

Ravi, K. and Rajaram, M., Optimal location of FACTS devices using Improved Particle Swarm Optimization, Int. J. Electr. Power Energy Syst., 49(1), pp. 333-338, 2013.

Manganuril, Y., Optimal location of TCSC using sensitivity and stability indices for reduction in losses and improving the Voltage profile, pp. 1-4, 2016.

Taleb, M., Mieee, A.S., Mieee, A.A. and Azma, M.A., Optimal allocation of TCSC using adaptive cuckoo search algorithm, pp. 7-11, 2016.

Singh, S.N. and David, A.K., Optimal location of FACTS devices for congestion management., Electr. Power Syst. Res., 58(2), pp. 71-79, 2001. DOI: 10.1016/S0378-7796(01)00087-6.

Vardakas, J.S., Zorba, N. and Verikoukis, C.V., A survey on demand response programs in smart grids: Pricing methods and optimization algorithms., IEEE Commun. Surv. Tutorials, 17(1), pp. 152-178, 2015. DOI: 10.1109/COMST.2014.2341586

Wang, Q., Zhang, C., Ding, Y., Xydis, G., Wang, J. and Østergaard, J., Review of real-time electricity markets for integrating distributed energy resources and demand response, Appl. Energy, 138, pp. 695-706, 2015. DOI: 10.1016/j.apenergy.2014.10.048.

Pandzic, H., Wang, Y., Qiu, T., Dvorkin, Y. and Kirschen, D.S., Near-optimal method for siting and sizing of distributed storage in a transmission network, IEEE Trans. Power Syst., 30(5), pp. 2288-2300, 2015.

Murillo-Sánchez, C.E., Zimmerman, R., Anderson, L. and Thomas, R.J., Secure planning and operations of systems with stochastic cources, energy storage and active demand, IEEE Trans. Smart Grid, 4(4), pp. 2220-2229, 2013.

Papavasiliou, A., Oren, S.S. and Rountree, B., Applying high performance computing to transmission-constrained stochastic unit commitment for renewable energy integration, pp. 1-12, 2014. DOI: 1109/TPWRS.2014.2341354.

Sáiz-Marín, E., Lobato, E. and Egido, I., Optimal voltage control by wind farms using data mining techniques, IET Renew. Power Gener. April, pp. 141-150, 2013. DOI: 10.1049/iet-rpg.2013.0025.

Panda, A. and Tripathy, M., Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm, Energy, 93, pp. 816-827, 2015. DOI: 10.1016/j.energy.2015.09.083

Saunders, C.S., Point estimate method addressing correlated wind power for probabilistic optimal power flow, IEEE Trans. Power Syst., 29(3), pp. 1045-1054, 2014.

Kaur, M., Newton’s method approach for security constrained OPF using TCSC, en 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, 2016. DOI: 10.1109/ICPEICES.2016.7853437

Dimensions

PlumX

Visitas a la página del resumen del artículo

1753

Descargas

Los datos de descarga aún no están disponibles.

Cómo citar

[1]
“Modelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisión”, DYNA, vol. 84, no. 202, pp. 102–111, Jul. 2017, doi: 10.15446/dyna.v84n202.63354.

Licencia

Derechos de autor 2017 DYNA

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.

El autor o autores de un artículo aceptado para publicación en cualquiera de las revistas editadas por la facultad de Minas cederán la totalidad de los derechos patrimoniales a la Universidad Nacional de Colombia de manera gratuita, dentro de los cuáles se incluyen: el derecho a editar, publicar, reproducir y distribuir tanto en medios impresos como digitales, además de incluir en artículo en índices internacionales y/o bases de datos, de igual manera, se faculta a la editorial para utilizar las imágenes, tablas y/o cualquier material gráfico presentado en el artículo para el diseño de carátulas o posters de la misma revista.